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Selective layer looping in masked diffusion language models unlocks surprising gains in both training efficiency and reasoning performance, beating naive depth scaling.
VLMs still struggle with subtle visual differences, exhibiting a significant gap compared to human-level comparative reasoning across diverse domains like medical and industrial imaging.